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6.2.1.1 Pulse Amplitude Modulation (PAM) Wave. With several exceptions, practical PCM systems involve time-division multiplexing. Sampling in these cases does not just involve one voice channel but several. In practice, one system (T1) samples 24 voice channels in sequence and another (E1) samples 30 voice channels. The result of the multiple sampling is a pulse amplitude modulation (PAM) wave. A simpli ed PAM wave is shown in Figure 6.1. In this case, it is a single sinusoid (sine wave). A simpli ed diagram of the processing involved to derive a multiplex PAM wave is shown in Figure 6.2. For simplicity, only three voice channels are sampled sequentially. The sampling is done by gating. It is just what the term means, a gate is opened for a very short period of time, just enough time to obtain a voltage sample. With the North American DS1 (T1) system, 24 voice channels are sampled sequentially and are interleaved to form a PAM-multiplexed wave. The gate is open about 5.2 ms (125/ 24) for each voice. The full sequence of 24 channels is sampled successively from channel 1 through channel 24 in a 125-ms period. We call this 125-ms period a frame, and inside the frame all 24 channels are successively sampled just once. Another system widely used outside of the United States and Canada is E1, which is a 30-voice-channel system plus an additional two service channels, for a total of 32 channels. By de nition, this system must sample 8000 times per second because it is also optimized for voice operation, and thus its frame period is 125 ms. To accommodate the 32 channels, the gate is open 125/ 32 or about 3.906 ms. 6.2.2

Proof: Consider the lower bound. The allowed programs satisfy the pre x property, and thus their lengths satisfy the Kraft inequality. We assign to each x n the length of the shortest program p such that U(p, n) = x n . These shortest programs also satisfy the Kraft inequality. We know from the theory of source coding that the expected codeword length must be greater than the entropy. Hence, f (x n )K(x n |n) H (X1 , X2 , . . . , Xn ) = nH (X).

real-time, 400 recalibration, 414 recognition 337, 338, 340, 344, 345, 347, 350, 354, 355, 357, 359 reconstruction criterion, 56 Recursive, density estimation (RDE), 24 least squares (RLS), 35 reference model, 116 re nery, 399 regression, 41 regularization, 99 residuals, 116 retraining, 41, 121, 415 Robotics, autonomous, 367 behavior-based, 382, 386 evolutionary, 367, 386 hand 337, 338 robustness, 37, 88, 403 rolling mill, 113 r2 statistics, 117, 319 rule-based model, 52 Rule, age, 29, 107 active, 103 chain, 180 competition, 28 cooperation, 28 evolution, 29, 94 ring degree, 102 removal, 31 update, 41, 94 SAFIS, 89 scheduling, 401 segmentation 338, 345, 362 sensitivity analysis, 38, 118, 119, 313, 321, 323 self-calibrating sensors, 313 self-maintaining sensors, 313, 315 self-developing systems, 40 sliding regression model, 117 shift, concept, 30, 104, 402 simpl_eTS, 48, 89 similarity analysis, 247, 248, 257, 258 similarity degree 247, 248, 257, 263, 349 simulation 350, 351 SOFNN, 89

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So what do you do once fraud has occurred As a merchant, who do you contact to report the crime How do you try to prove your case to the issuing bank When you nd that a fraudulent transaction has occurred, you can report it in several ways. One of the ways is to use an online resource called the Internet Fraud Complaint Center (IFCC). The IFCC is a partnership between the Federal Bureau of Investigation (FBI) and the National White Collar Crime Center (NW3C). The IFCC s mission is to address fraud committed over the Internet. For victims of Internet fraud, IFCC provides a convenient and easy-touse reporting mechanism that alerts authorities of a suspected criminal or civil violation. For law enforcement and regulatory agencies at all levels, IFCC offers a central repository for complaints related to Internet fraud, works to quantify fraud patterns, and provides timely statistical data of current fraud trends.